Overview
Exploratory runs were carried out to see how selection schemes perform at a fundamental level.
The results of these run are divded by diagnotic problem, and further by selection scheme.
Diagnostics
For this diagnostic there were 100 objectives to optimize, all with the target of 100.
The population was size 512, and there was a point mutation probability of .7% at each objective.
Exploitation
Mu Lambda Selection
Population Aggregate Fitness Average

Population Aggregate Fitness Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Tournament Selection
Population Aggregate Fitness Average

Population Aggregate Fitness Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Lexicase Selection
Population Aggregate Fitness Average

Population Aggregate Fitness Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Structured Exploitation
Mu Lambda Selection
Population Aggreagate Fitness

Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Tournament Selection
Population Aggreagate Fitness

Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Lexicase Selection
Population Aggreagate Fitness

Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Contradictory Ecology
Mu Lambda Selection
Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Tournament Selection
Population Aggreagate Fitness

Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Lexicase Selection
Population Aggreagate Fitness

Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Exploration
Mu Lambda Selection
Population Aggreagate Fitness

Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Tournament Selection
Population Aggreagate Fitness

Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure

Lexicase Selection
Population Aggreagate Fitness

Population Optimal Count Average

Population Unique Optimal Count Average

Common Solution Count

Common Solution Optimization Count

Elite Solution Optimization Count

Optimal Solution Optimization Count

Loss in Diversity

Selection Pressure
